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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Related Experiment Video

Updated: Jun 16, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Active learning for human protein-protein interaction prediction.

Thahir P Mohamed1, Jaime G Carbonell, Madhavi K Ganapathiraju

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. mop13+bmc@pitt.edu

BMC Bioinformatics
|February 4, 2010
PubMed
Summary
This summary is machine-generated.

Active learning significantly improves protein-protein interaction prediction accuracy. This method requires fewer experimentally validated protein pairs, accelerating the mapping of the human protein interactome.

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Last Updated: Jun 16, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Published on: January 26, 2024

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Biological processes rely on protein-protein interactions (PPIs).
  • Experimental PPI determination is resource-intensive, with limited known interactions.
  • Predicting the human protein interactome is crucial but challenging.

Purpose of the Study:

  • To develop and evaluate active learning algorithms for PPI prediction.
  • To enhance the efficiency of training protein-protein interaction classifiers.
  • To accelerate the accurate prediction of the human protein interactome.

Main Methods:

  • Four active learning algorithms were developed to select protein pairs for training.
  • Random Forest (RF) was used as the classifier for PPI prediction.
  • Comparison of active learning selection versus random selection of training data.

Main Results:

  • Active learning methods achieved higher F-scores with significantly less labeled data (500 pairs) compared to random selection (3000 pairs).
  • An approximate 15% increase in F-score was observed using active learning over random selection.
  • The developed algorithms effectively guided the selection of informative protein pairs for training.

Conclusions:

  • Active learning algorithms enhance classifier accuracy with reduced labeled data, ideal for formidable annotation tasks.
  • These techniques are valuable for accelerating PPI prediction and can be applied to other proteomics areas.
  • Potential applications include protein structure prediction and classification.